Biology subjects

Moni, M. A.

Publications and source records attributed to Moni, M. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Global dissemination of FC-ϵ RI and p53 signalling perturbation contribute to Dasatinib resistance in Pancreatic Cancer Cell-lines

Acquired Dasatinib Resistance (ADR) hinders efficacious treatment of Pancreatic Cancer (PC), often mediated by dynamic signalling reprogramming due to prolonged drug intake. With a novel signalling cross-talk network modelling, this study analyses transcriptomics data of dasatinib-resistant and dasatinib-sensitive pancreatic cancer cell lines and prioritizes key signalling molecules via systemic coordination of their magnitude of dysregulation and the degree of signalling cross-talk among enriched pathways. Results found the p53 and FC-{epsilon} RI signalling pathways demonstrating significant perturbation enrichment, complementarily orchestrating a total of 87% of the global perturbation map in dasatinib resistance. Further statistical characterization of the cross-talk network identified 10 key resistant biomarkers, including THBS1, CDKN1A, and BCL2L1 within p53 signalling, and RAC2 and MAPK13 within FC epsilon RI signalling. Validation with TCGA transcriptomics, CPTAC relative proteomics, and StringDB protein-protein interaction data for their potential prognostics revealed BCL2L1 as pivotal for global perturbation dissemination and, thereby, a novel therapeutic target.

bioinformatics↗

bioRxiv2024

Towards Reliable Diabetes Prediction: Innovations in Data Engineering and Machine Learning Applications

ObjectiveDiabetes is a metabolic disorder that causes the risk of stroke, heart disease, kidney failure, and other long-term complications because diabetes generates excess sugar in the blood. Machine learning (ML) models can aid in diagnosing diabetes at the primary stage. So, we need an efficient machine learning model to diagnose diabetes accurately. MethodsIn this paper, an effective data preprocessing pipeline has been implemented to process the data and random oversampling to balance the data, handling the imbalance distributions of the observational data more sophisticatedly. We used four different diabetes datasets to conduct our experiments. Several ML algorithms were used to determine the best models to predict diabetes faultlessly. ResultsThe performance analysis demonstrates that among all ML algorithms, RF surpasses the current works with an accuracy rate of 86% and 98.48% for dataset-1 and dataset-2; XGB and DT surpass with an accuracy rate of 99.27% and 100% for dataset-3 and dataset-4 respectively. Our proposal can increase accuracy by 12.15% compared to the model without preprocessing. ConclusionsThis excellent research finding indicates that the proposed models might be employed to produce more accurate diabetes predictions to supplement current preventative interventions to reduce the incidence of diabetes and its associated costs.

bioinformatics↗

bioRxiv2022

Exploring different virulent proteins of human respiratory syncytial virus for designing a novel epitope-based polyvalent vaccine: Immunoinformatics and molecular dynamics approaches

Human Respiratory Syncytial Virus (RSV) is one of the most prominent causes of lower respiratory tract infections (LRTI), contributory to infecting people from all age groups - a majority of which comprises infants and children. The implicated severe RSV infections lead to numerous deaths of multitudes of the overall population, predominantly the children, every year. Consequently, despite several distinctive efforts to develop a vaccine against the RSV as a potential countermeasure, there is no approved or licensed vaccine available yet, to control the RSV infection effectively. Therefore, through the utilization of immunoinformatics tools, a computational approach was taken in this study, to design and construct a multi-epitope polyvalent vaccine against the RSV-A and RSV-B strains of the virus. Potential predictions of the T-cell and B-cell epitopes were followed by extensive tests of antigenicity, allergenicity, toxicity, conservancy, homology to human proteome, transmembrane topology, and cytokine-inducing ability. The most promising epitopes (i.e. 13 CTL epitopes, 9 HTL epitopes, and 10 LBL epitopes) exhibiting full conservancy were then selected for designing the peptide fusion with appropriate linkers, having hBD-3 as the adjuvant. The peptide vaccine was modeled, refined, and validated to further improve the structural attributes. Following this, molecular docking analysis with specific TLRs was carried out which revealed excellent interactions and global binding energies. Additionally, molecular dynamics (MD) simulation was conducted which ensured the stability of the interactions between vaccine and TLR. Furthermore, mechanistic approaches to imitate and predict the potential immune response generated by the administration of vaccines were determined through immune simulations. Owing to an overall evaluation, in silico cloning was carried out in efforts to generate recombinant pETite plasmid vectors for subsequent mass production of the vaccine peptide, incorporated within E.coli. However, more in vitro and in vivo experiments can further validate its efficacy against RSV infections.

immunology↗

bioRxiv2020

A Computational Approach to Design Potential siRNA Molecules as a Prospective Tool for Silencing Nucleocapsid Phosphoprotein and Surface Glycoprotein Gene of SARS-CoV-2

An outbreak, caused by a RNA virus, SARS-CoV-2 named COVID-19 has become pandemic with a magnitude which is daunting to all public health institutions in the absence of specific antiviral treatment. Surface glycoprotein and nucleocapsid phosphoprotein are two important proteins of this virus facilitating its entry into host cell and genome replication. Small interfering RNA (siRNA) is a prospective tool of the RNA interference (RNAi) pathway for the control of human viral infections by suppressing viral gene expression through hybridization and neutralization of target complementary mRNA. So, in this study, the power of RNA interference technology was harnessed to develop siRNA molecules against specific target genes namely, nucleocapsid phosphoprotein gene and surface glycoprotein gene. Conserved sequence from 139 SARS-CoV-2 strains from around the globe was collected to construct 78 siRNA that can inactivate nucleocapsid phosphoprotein and surface glycoprotein genes. Finally, based on GC content, free energy of folding, free energy of binding, melting temperature and efficacy prediction process 8 siRNA molecules were selected which are proposed to exerts the best action. These predicted siRNAs should effectively silence the genes of SARS-CoV-2 during siRNA mediated treatment assisting in the response against SARS-CoV-2

bioinformatics↗

bioRxiv2020

In Silico Identification, Characterization and Diversity Analysis of RNAi Genes and their Associated Regulatory Elements in Sweet Orange (Citrus sinensis L)

RNA interference (RNAi) plays key roles in post-transcriptional and chromatin modification levels as well as regulates various eukaryotic gene expressions which involved in stress responses, development and maintenance of genome integrity during developmental stages. The whole mechanism of RNAi pathway is directly involved with the gene-silencing process by the interaction of Dicer-Like (DCL), Argonaute (AGO) and RNA-dependent RNA polymerase (RDR) gene families. However, the genes of these three RNAi families are largely unknown yet in sweet orange (Citrus sinensis), though it is an economically important fruit plant all over the world. Therefore, a comprehensive investigation for genome-wide identification, characterization and diversity analysis of RNA silencing genes in C. sinensis was conducted and identified 4 CsDCL, 8 CsAGO and 4 CsRDR as RNAi genes. To characterize and validate the predicted genes of RNAi families, various bioinformatics analysis was conducted. Phylogenetic analysis clustered the predicted CsDCLs, CsAGOs and CsRDRs genes into four, six and four subgroups with the relevant genes of Arabidopsis respectively. The domain and motif composition analysis, the gene structure for all three-gene families exhibited almost homogeneity within the same group members while showed significant differences in between groups. The GO enrichment analysis results clearly indicated that the predicted genes have direct involvement into the RNAi process as expected in C. sinensis. Moreover, Cis-regulatory elements and regulatory transcription factor analysis of the reported RNAi genes demonstrated the diverse connection to the huge biological functions and regulatory pathways. The expressed sequence tag (EST) analysis showed that these genes are highly expressed in fruit and leaves which indicate that these reported genes have great involvement in C. sinensis food, flowering and fruit production. The expression analysis of the reported RNAi genes might be more useful to explore the most effective RNAi genes in C. sinensis for further biotechnological application.

genomics↗

bioRxiv2019

Protein Expression Patterns in ovarian cancer cells Associated with Monofunctional Platinums Treatment

Platinum drugs cisplatin and carboplatin, given in combination with paclitaxel, constitute the standard chemotherapy against ovarian cancer (OC). Oc chemoresistance is a major obstacle to effective treatment, but knowledge of the mechanisms that underlie it remains incomplete. We thus sought to discover key proteins associated with platinum resistance by comparing A2780 OC cells with A2780cisR cells (resistant cells derived from the A2780 line) to identify proteins with markedly altered expression levels in the resistant cells. We also determined which proteins in these cells had altered expression in response to treatment with either designed monofunctional platinum alone or a combination with cisplatin with selected phytochemical therapeutic agents.\n\nWe thus performed proteomic analysis using 2D-gel electrophoresis A2780 and A2780cisR to identify proteins with differential expression; these were eluted and analysed by mass spectrometry to identify them. A total of 122 proteins were found to be differentially expressed between A2780 and A2780cisR cell lines in the absence of any drug treatment. Among them, levels of 27 proteins in A2780cisR cell line were further altered (up-or down-regulated) in response to one or more of the drug treatments. We then investigated primary OC tissue RNA expression levels (compared to l ovarian tissue) of genes coding for these candidate 27 proteins using publically available datasets (The Cancer Genome Atlas). We assessed how expression of these genes in OC tissue associates with patient survival using Cox Proportional Hazard (PH) regression models to determine relative risk of death associated with each factor. Our Cox PH regression-based machine learning method confirmed a significant relationship of mortality with altered expression of ARHGDIA, CCT6A and HISTIH4F genes. This indicated that these genes affect OC patient survival, i.e., provided mechanistic evidence, in addition to that of the clinical traits, that these genes may be critical mediators of the processes that underlie OC progression and mortality.\n\nThus, we identified differentially expressed proteins that are implicated in platinum-based chemotherapy resistance mechanisms which may serve as resistance biomarkers. These drug resistance associated proteins may also serve as potential OC therapeutic targets whose blockade may enhance the effectiveness of platinum based drugs.

cancer biology↗

bioRxiv2018

Deciphering the molecular pathways of apoptosis using leaf extract of Basella alba against Ehrlichs Ascites Carcinoma (EAC) cell line in Swiss albino mice model

A series of programmed cell death that plays a vital role in the exclusion of abnormal cells without any ruin into the surrounding neighboring cells is called Apoptosis. Generally, it occurs in multi-cellular organisms through an orderly and autonomously processes that is controlled by proper function of various genes. In the current studies, cell apoptosis in EAC cells treated with different fractions (BLP-01, BLP-02 and BLP-03) of leaf extract of B. alba was detected by conducting several bio-assays such as cell growth inhibition, fluorescence and optical microscopy, DNA fragmentation and PCR amplification etc. The results of these experiments indicate that the plant extracts are able to inhibit cell growth significantly where mor- phological features of apoptosis were appeared under both fluorescence and optical microscope. The PCR amplification results showed that the leaf extracts of B. alba were able to cause EAC cell apoptosis in both the extrinsic and intrinsic pathway. An excellent figure of fragmented DNA was found in DNA fragmentation assay when the gel was observed under UV light which confirms the cell apoptosis. The current findings suggest that the samples of this experiment occupy fascinating competence to conduct cell apoptosis and become an ideal resource for cancer research as well as drugs development for cancer treatment.

molecular biology↗

bioRxiv2018

Genetic Effects of Welding Fumes on the progression ofNeurodegenerative Diseases

BackgroundWelding exposes different types of fumes, gases and radiant energy that can be potentially dangerous for unsafe welders health. Welding fumes (WFs) are a significant problem among all those exposed. WFs are a complex mixture of metallic oxides, silicates and fluorides that may result in different health effects. If a welder inhales such fumes in large quantities over a long period of time, there is a risk of various neurodegenerative diseases (NDGDs) development.\n\nMethodsWe developed quantitative frameworks to identify the genetic relationship of WFs and NDGDs. We analyzed Gene Expression microarray data from WFs exposed tissues and NDGDs including Parkinsons disease (PD), Alzheimers disease (AD), Lou Gehrigs disease (LGD), Epilepsy disease (ED), Multiple Sclerosis disease (MSD) datasets. We constructed disease-gene relationship networks and identified dysregulated pathways, ontological path- ways and protein-protein interaction sub-network using multilayer network topology and neighborhood-based benchmarking.\n\nResultsWe observed that WFs shares 18, 16, 13, 19 and 19 differentially expressed genes with PD, AD, LGD, ED and MSD respectively. Gene expression dysregulation along with relationship networks, pathways and ontologic analysis showed that WFs are responsible for the progression of PD, AD, LGD, ED and MSD neurodegenerative diseases.\n\nConclusionOur developed network-based approach to analysis and investigate the genetic effects of welding fumes on PD, AD, LGD, ED and MSD neurodegenerative diseases could be helpful to understand the causal influences of WF exposure for the progression of the NDGDs.

bioinformatics↗

bioRxiv2018

Genetic Effects of Welding Fumes to the development ofRespiratory System Diseases

BackgroundThe welding process releases potentially hazardous gases and fumes, mainly composed of metallic oxides, fluorides and silicates. Long term welding fume (WF) inhalation is a recognized health issue that carries a risk of developing chronic health problems, particularly respiratory system diseases (RSDs). Aside from general airway irritation, WFs may drive direct cellular responses in the respiratory system which increase risk of RSD, but these are not well understood.\n\nMethodsWe developed a quantitative framework to identify gene expression effects of WFs that may affect RSD development. We analyzed gene expression microarray data from WF-exposed tissues and RSD-affected tissues, including chronic bronchitis (CB), asthma (AS), pulmonary edema (PE), lung cancer (LC) datasets. We built disease-gene (disea-some) association networks and identified dysregulated signaling and ontological pathways, and protein-protein interaction sub-network using neighborhood-based benchmarking and multilayer network topology.\n\nResultsWe observed many genes with altered expression in WF-exposed tissues were also among differentially expressed genes (DEGs) in RSD tissues; for CB, AS, PE and LC there were 34, 27, 50 and 26 genes respectively. DEGs analysis, with disease association networks, pathways, ontological analysis and protein-protein interaction sub-network suggest significant links between WF exposure and the development of CB, AS, PE and LC.\n\nConclusionsOur network-based analysis and investigation of the genetic links of WFs and RSDs confirm a number of genes and gene products are plausible participants in RSD development. Our results are a significant resource to identify causal influences on the development of RSDs, particularly in the context of WF exposure.

bioinformatics↗

bioRxiv2018

Mining Significant Features of Diabetes Mellitus Applying Decision Trees: A Case Study In Bangladesh

Diabetes is a chronic condition which is associated with an abnormally high level of sugar in the blood. It is a lifelong disease that causes harmful effects in human life. The goal of this research is to predict the severity of diabetes and find out significant features of it. In this work, we gathered diabetes patients records from Noakhali Diabetes Association, Noakhali, Bangladesh. Thus, We preprocessed our raw dataset by replacing and removing missing and wrong records respectively. Thus, CDT, J48, NBTree and REPtree decision tree based classification techniques were used to analyze this dataset. After this analysis, we evaluated classification outcomes of these decision tree classifiers and found the best decision tree model from them. In this work, CDT unpruned tree shows highest accuracy, precision, recall, f-measure, second highest AUROC and lowest RMSE than other models. Then, we extracted possible rules and significant features from this model and plasma glucose, plasma glucose 2hr after glucose and HDL-cholesterol have been found as the most significant features to predict the severity of Diabetes Mellitus. We hope this work will be beneficial to build a predictive system and complementary tool for diabetes treatment in future.

bioengineering↗

bioRxiv2018

A Network based Approach to Identify the Genetic Influence Caused by Associated Factors and Disorders for the Parkinsons Disease Progression

Actual causes of Parkinsons disease (PD) are still unknown. In any case, a better comprehension of genetic and ecological influences to the PD and their interaction will assist physicians and patients to evaluate individual hazard for the PD, and definitely, there will be a possibility to find a way to reduce the progression of the PD. We introduced quantitative frameworks to reveal the complex relationship of various biasing genetic factors for the PD. In this study, we analyzed gene expression microarray data from the PD, ageing (AG), severe alcohol consumption (AC), type II diabetes (T2D), high body fat (HBF), hypercholesterolemia (HC), high dietary fat (HDF), red meat dietary (RMD), sedentary lifestyle (SL), smoking (SM), and control datasets. We have developed genetic associations of various factors with the PD based on the neighborhood-based benchmarking and multilayer network topology.\n\nWe identified 1343 significantly dysregulated genes in the PD patients compared to the healthy control, where we have 779 genes down regulated and 544 genes up regulated. 69 genes were highly expressed in both for the PD and alcohol consumption whereas the number of shared genes for the PD and the type II diabetes is 51. However, the PD shared 45, 43 and 42 significantly expressed genes with the ageing, high dietary fat and high body fat respectively. The PD shared less than 40 significant transcripts with other factors. Ontological and pathway analyses have identified significant gene ontology and molecular pathways that enhance our understanding of the fundamental molecular procedure of the PD progression. Therapeutic targets of the PD could be developed using these identified target genes, ontologies and pathways. Our formulated methodologies demonstrate a network-based approach to understand the disease mechanism and the causative reason of the PD, and the identification for therapeutic targets of the PD.

neuroscience↗

bioRxiv2018

Genetic effect of type 2 Diabetes to the progression of Neurological Diseases

Neurological Diseases (NDs) are progressive disorder often advances with age and comorbidities of Type 2 diabetes (T2D). Epidemiological, clinical and neuropathological evidence advocate that patients with T2D are at an increased risk of getting NDs. However, it is very little known how T2D affects the risk and severity of NDs.\n\nTo tackle these problems, we employed a transcriptional analysis of affected tissues using agnostic approaches to identify overlapping cellular functions. In this study, we examined gene expression microarray human datasets along with control and disease-affected individuals. Differentially expressed genes (DEG) were identified for both T2D and NDs that includes Alzheimer Disease (AD), Parkinson Disease (PD), Amyotrophic Lateral Sclerosis (ALS), Epilepsy Disease (ED), Huntington Disease (HD), Cerebral Palsy (CP) and Multiple Sclerosis Disease (MSD).\n\nWe have developed genetic association and diseasome network of T2D and NDs based on the neighborhood-based benchmarking and multilayer network topology approaches. Overlapping DEG sets go through protein-protein interaction for hub protein identification and gene enrichment using pathway analysis and gene ontology methods that enhance our understanding of the fundamental molecular procedure of NDs progression.\n\nGene expression analysis platforms have been extensively used to investigate altered pathways and to identify potential biomarkers and drug targets. Finally, we validated our identified biomarkers using the gold benchmark datasets which identified the corresponding relationship of T2D and NDs. Therapeutic targets aimed at attenuating identified altered pathway could ameliorate neurological dysfunction in a T2D patient.

bioinformatics↗

bioRxiv2018

Network-based genetic profiling, and therapeutic target identification of Thyroid Cancer

Pathogenic mechanisms that underlie malignant follicular thyroid carcinoma (FTC) development are poorly understood. To identify key genes and pathways driving malignant behaviour we employed a system biology-based integrative analyses comparing FTC transcriptomes with a similar but benign lesion, follicular thyroid adenoma (FTA). We identified differentially expressed genes (DEGs) in microarray gene expression datasets (n=52) of FTCs and FTA tissues. Pathway analyses of DEGs using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources revealed significant pathways, and pathway hub genes using protein-protein interactions (PPI). We identified 598 DEGs (relative to FTAs) in FTCs and 12 significant pathways with altered expression in FTC. 10 GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression. PPI analysis identified 12 potential hub genes based on degree and betweenness centrality. Moreover, 10 transcription factors (TFs) were identified that may underlie DEG expression as well as a number of microRNA (miRNAs). Thus, we identified DEGs, pathways, TFs and miRNAs that reflect molecular mechanisms differing between FTC and benign FTA. These may constitute biomarkers that distinguish these lesions and, given the similarities and common origin of the lesions, they may also be indicators of malignant progression potential.

bioinformatics↗

bioRxiv2018

Blood-based molecular biomarker signatures in Alzheimer’s disease: Insights from systems biomedicine analyses

Background and objectivesAlzheimers disease (AD) is the progressive neurodegenerative disease characterized by dementia, but no peripheral biomarkers available yet that can detect the AD. This study aimed to identify systems biomarker signatures in the AD through integrative analyses.\n\nMaterials and MethodsWe used two microarray transcriptomics datasets of blood from AD patients to identify differentially expressed genes (DEGs). Geneset and protein overrepresentation analysis, protein-protein interaction (PPI), DEGs-Transcription Factor interactions, DEGs-MicroRNAs interactions, protein-drug interactions, and protein subcellular localizations analyses were done on common DEGs.\n\nResultsTotal 25 DEGs were detected between the two datasets. Integration of DEGs with biomolecular networks revealed hub proteins (TUBB, ATF3, NOL6, UQCRC1, SND1, CASP2, BTF3, INPP5K, VCAM1, and CSTF1), TFs (FOXC1, ZNF3, GEMIN7, and SMG9), miRNAs (mir-20a-5p, mir-93-5p, mir-16-5p, let-7b-5p, mir-708-5p, mir-24-3p, mir-26b-5p, mir-17-5p, mir-4270, and mir-4441). The analyses revealed candidate blood based biomarkers in the AD. We evaluated the histone modifications of the identified biomolecules. The hub genes and transcription factors (TFs) revealed that they possess several histone modification sites associated with Alzheimers disease. The protein-drug interactions revealed 10 candidate drugs consisting of antineoplastic (Vinorelbine, Vincristine, Vinblastine, Epothilone D, Epothilone B, CYT997, and ZEN-012), dermatologicals (Podofilox), and immunosuppressive agents (Colchicine) that may target the candidate systems biomarkers. The subcellular localization analysis revealed the interactions of the DEGs range from nucleus to plasma membrane through cytosol.\n\nConclusionsThis study presents blood based systems molecular biomarker signatures at RNA and protein levels which might be useful as peripheral biomarkers in the AD. The candidate drugs, histone modification sites, and subcellular localizations will be useful in future drug design in the AD.

systems biology↗

bioRxiv2018

Common molecular biomarker signatures in blood and brain of Alzheimers disease

BackgroundAlzheimers disease (AD) is a progressive neurodegenerative disease characterized by memory loss and confusion. Neuroimaging and cerebrospinal fluid-based early detection is limited in sensitivity and specificity as well as by cost. Therefore, detecting AD from blood cell analysis could improve early diagnosis and treatment of the disease. The present study aimed to identify blood cell transcripts that reflect brain expression levels of factors linked to AD progression.\n\nMethodsWe analyzed blood cell and brain microarray gene expression datasets from NCBI-GEO for AD association and expression in blood and brain. We also used eQTL and epigenetics data to identify AD-related genes that were regulated similarly in blood and brain.\n\nResultsWe identified 9 differentially expressed genes (DEG; AD versus controls) common to blood cells and brain (CNBD1, SUCLG2-AS1, CCDC65, PDE4D, MTMR1, C3, SLC6A15, LINC01806, and FRG1JP) and 18 genes (HSD17B1, GAS5, RPS5, VKORC1, GLE1, WDR1, RPL12, MORN1, RAD52, SDR39U1, NPHP4, MT1E, SORD, LINC00638, MCM3AP-AS1, GSDMD, RPS9, and GNL2) that were commonly dysregulated between AD blood and brain tissues using SNP and cis-eQTL data. This data revealed significant neurodegeneration-associated molecular pathways in the ribosomal and complement systems. Integration of these different analyses revealed dys-regulation of hub transcription factors (SREBF2, NR1H2, NR1H3, PRDM1, XBP1) and microRNAs (miR-518e, miR-518a-3p, miR-518b, miR-518c, miR-518d-3p and miR-518f) in AD. Several significant histone modification sites in DEGs were also identified.\n\nConclusionWe have identified new putative links between pathological processes in brain and transcripts in blood cells in AD subjects that may enable the use of blood to diagnose and monitor AD onset and progression.

neuroscience↗

bioRxiv2018

Network-based identification of genetic factors in Ageing, lifestyle and Type 2 Diabetes that Influence in the progression of Alzheimer’s disease

MotivationAlzheimers disease (AD) is currently incurable and the causative risk factors are still poorly understood, which impedes development of effective prevention and treatment strategies. We propose a network-based quantitative framework to reveal details of the complex interaction between the various genetic contributors to AD susceptibility. We analyzed gene expression microarray data from tissues affected by AD, advanced ageing, high alcohol consumption, type II diabetes, high body fat, high dietary fat, obesity, high dietary red meat intake, sedentary lifestyle, smoking, and control datasets. We developed genetic associations and diseasome networks for these factors and AD using the neighborhood-based benchmarking and multilayer network topology approaches.\n\nResultsThe study identified 484 genes differentially expressed between AD and controls. Among these, 27 genes showed elevated expression both in individuals in AD and in smoker datasets; similarly 21 were observed in AD and type II diabetes datasets and 12 for AD and sedentary lifestyle datsets. However, AD shared less than ten such elevated expression genes with other factors examined. 3 genes, namely HLA-DRB4, IGH and IGHA2 showed increased expression among the AD, type II diabetes and alcohol consumption datasets; 2 genes, IGHD and IGHG1, were commonly up-regulated among the AD, type II diabetes, alcohol consumption and sedentary lifestyle datasets. Protein-protein interaction networks identified 10 hub genes: CREBBP, PRKCB, ITGB1, GAD1, GNB5, PPP3CA, CABP1, SMARCA4, SNAP25 and GRIA1. Ontological and pathway analyses genes, including Online Mendelian Inheritance in Man (OMIM) and dbGaP databases were used for gold benchmark gene-disease associations to validate the significance of these putative target genes of AD progression.\n\nConclusionOur network-based methodologies have uncovered molecular pathways that may influence AD development, suggesting novel mechanisms that contribute to AD risk and which may form the basis of new therapeutic and diagnostic approaches.\n\nContactmohammad.moni@sydney.edu.au

molecular biology↗

bioRxiv2018

Early Detection of Neurological Dysfunction Using Blood Cell Transcript Profiles

Identification of genes whose regulation of expression is similar in both brain and blood cells could enable monitoring of significant neurological traits and disorders by analysis of blood samples. We thus employed transcriptional analysis of pathologically affected tissues, using agnostic approaches to identify overlapping gene functions and integrating this transcriptomic information with expression quantitative trait loci (eQTL) data. Here, we estimate the correlation of genetic expression in the top-associated cis-eQTLs of brain tissue and blood cells in Parkinsons (PD). We introduced quantitative frameworks to reveal the complex relationship of various biasing genetic factors in PD, a neurodegenerative disease. We examined gene expression microarray and RNA-Seq datasets from human brain and blood tissues from PD-affected and control individuals. Differentially expressed genes (DEG) were identified for both brain and blood cells to determine common DEG overlaps. Based on neighborhood-based benchmarking and multilayer network topology aproaches we then developed genetic associations of factors with PD. Overlapping DEG sets underwent gene enrichment using pathway analysis and gene ontology methods, which identified candidate common genes and pathways. We identified 12 significantly dysregulated genes shared by brain and blood cells, which were validated using dbGaP (gene SNP-disease linkage) database for gold-standard benchmarking of their significance in disease processes. Ontological and pathway analyses identified significant gene ontology and molecular pathways that indicate PD progression. In sum, we found possible novel links between pathological processes in brain and blood cells by examining cell path-way commonalities, corroborating these associations using well validated datasets. This demonstrates that for brain-related pathologies combining gene expression analysis and blood cell cis-eQTL is a potentially powerful analytical approach. Thus, our methodologies facilitate data-driven approaches that can advance knowledge of disease mechanisms and may enable prediction of neurological dysfunction using blood cell transcript profiling.

bioinformatics↗

bioRxiv2018

Bioinformatics Approach to Identify Diseasome and Comorbidities Effect of Mitochondrial Dysfunctions on the Progression of Neurological Disorders

Mitochondrial dysfunction can cause various neurological diseases. We therefore developed a quantitative framework to explore how mitochondrial dysfunction may influence the progression of Alzheimers, Parkinsons, Huntingtons and Lou Gehrigs diseases and cerebral palsy through analysis of genes showing altered expression in these conditions. We sought insights about the gene profiles of mitochondrial and associated neurological diseases by investigating gene-disease networks, KEGG pathways, gene ontologies and protein-protein interaction network. Gene disease networks were constructed to connect shared genes which are commonly found between the neurological diseases and Mitochondrial Dysfunction. We also generated KEGG pathways and gene ontologies to explore functional enrichment among them, and protein-protein interaction networks to identify the shared protein groups of these diseases. Finally, we verified our biomarkers using gold benchmark databases (e.g., OMIM and dbGaP) which identified effective reasons of it. Our network-based methodologies are useful to investigate disease mechanisms, predictions for comorbidities and identified distinct similarities among different neurological disorders for mitochondrial dysfunction.

bioinformatics↗